Client Report - What’s in a Name?

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import pandas as pd
import numpy as np
from lets_plot import *

LetsPlot.setup_html(isolated_frame=True)

Project Notes

For Project 1 the answer to each question should include a chart and a written response. The years labels on your charts should not include a comma. At least two of your charts must include reference marks.

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# Learn morea about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html

# Include and execute your code here
df = pd.read_csv("https://github.com/byuidatascience/data4names/raw/master/data-raw/names_year/names_year.csv")

QUESTION|TASK 1

How does your name at your birth year compare to its use historically?

The name David peaked at 64,755 overall uses in the U.S. in 1955. This is 46,172 more uses than my birth year, 1999, which came in at 18583 total uses.

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# Include and execute your code here
import textwrap

my_name_1999 = df[(df['name'] == 'David') & (df['year'] == 1999)]

david_over_time = df[df['name'] == 'David']

david_arrow_label = textwrap.fill("18583 uses of 'David' in 1999", 20)

david_max = david_over_time[david_over_time['Total'] == david_over_time['Total'].max()]


dave_history = (ggplot(david_over_time, aes(x='year', y='Total'))
 + geom_point()
 + geom_point(data=my_name_1999, color='red', size=3)
 + geom_point(data=david_max, color='red', size=3)
 + xlim(1910, 2020)
 + ylim(0, 70000) 
 + labs(
     title="Popularity of the Name David Over Time in the U.S.",
     subtitle="(My birth year highlighted in red)",
     x='Year',
     y='Total Births'    
 )
 + geom_segment(
            x=2010, xend=1999,
            y=40000, yend=18584,
            arrow=arrow(type="closed"),
            color='red',
            size=1)
 + geom_label(x=1993, y=40000, label=david_arrow_label, hjust='left', color='red')
 + geom_label(x=1955, y=70000, label="64,755 uses of 'David' in 1955", color='red')
)
dave_history

QUESTION|TASK 2

If you talked to someone named Brittany on the phone, what is your guess of his or her age? What ages would you not guess?

The name Brittany spiked between the late 1980s and early 1990s so I would guess that his or her age would most likely span between her late 20s to her mid 30s. Anyone over 50 or under 15 years of age would not be likely to have that name so them I would not include in my guesswork.

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# Include and execute your code here
brittany_over_time = df[df['name'] == 'Brittany']

brittany_age_over_time = brittany_over_time[['year', 'Total']].copy()

brittany_age_over_time['Age'] = 2026 - brittany_age_over_time['year']

brittany_age_over_time

(ggplot(brittany_age_over_time, aes(x='year', y='Total')) +
 geom_line() +
 scale_x_continuous(format='d') +
 labs(title='Popularity of the Name Brittany Over Time',
      x='Year',
      y='Number of Brittanys Born') +
 theme_minimal() +
 theme(axis_line=element_line(size=1.5, color='black')))

QUESTION|TASK 3

Mary, Martha, Peter, and Paul are all Christian names. From 1920 - 2000, compare the name usage of each of the four names in a single chart. What trends do you notice?

Mary peaked in both the 1920s and the 1950s, while Martha, Peter, and Paul peaked between 1940 and 1965. After their individual peaks, all four names declined in usage through 2000.

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# Include and execute your code here

mary = df[(df['name'] == 'Mary') & (df['year'] >= 1920) & (df['year'] <= 2000)]
martha = df[(df['name'] == 'Martha') & (df['year'] >= 1920) & (df['year'] <= 2000)]
peter = df[(df['name'] == 'Peter') & (df['year'] >= 1920) & (df['year'] <= 2000)]
paul = df[(df['name'] == 'Paul') & (df['year'] >= 1920) & (df['year'] <= 2000)]

mary_plot = mary.copy()
martha_plot = martha.copy()
peter_plot = peter.copy()
paul_plot = paul.copy()

mary_plot['Name'] = 'Mary'
martha_plot['Name'] = 'Martha'
peter_plot['Name'] = 'Peter'
paul_plot['Name'] = 'Paul'

combined_df = pd.concat([mary_plot, martha_plot, peter_plot, paul_plot])

(ggplot(combined_df, aes(x='year', y='Total', color='Name')) +
    geom_line(size=1) +
    geom_point(size=2) +
    geom_vline(xintercept=1940, color='red', linetype='solid', size=1) +  
    geom_vline(xintercept=1965, color='red', linetype='solid', size=1) +  
    geom_rect(xmin=1940, xmax=1965, ymin=0, ymax=55000, 
              fill='red', alpha=0.1, inherit_aes=False) + 
    labs(title='Name Usage Comparison: Mary, Martha, Peter, and Paul (1920-2000)',
         x='Year',
         y='Total Usage') +
    theme_minimal() +
    scale_x_continuous(format='d'))

QUESTION|TASK 4

Think of a unique name from a famous movie. Plot the usage of that name and see how changes line up with the movie release. Does it look like the movie had an effect on usage?

type your results and analysis here

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# Include and execute your code here
bob_over_time = df[df['name'] == 'Bob']

incredibles_release_date = bob_over_time[df['year'] == 2004]

bob_label = textwrap.fill("6 uses of the name Bob the year of the Incredibles' release, 2004",25)

(
    ggplot(bob_over_time, aes(x='year', y='Total'))
    + geom_line()
    + scale_x_continuous(format='d') 
    + xlim(1910, 2020)
    + geom_point(data=incredibles_release_date, color='red', size=3)
    + geom_segment(x=1992, xend=2004, 
                  y=950, yend=6,
                  arrow=arrow(type='closed'),
                  color='red',
                  size=1)
    + geom_label(x=1990, y=971, label=bob_label, color='red')
    + labs (
        title=textwrap.fill("Popularity of Bob over time compared to the use of Bob (As in Bob Parr) at the time The Incredibles was released",50),
        x='Year',
        y='Number of Bobs Born'
    )
)

STRETCH QUESTION|TASK 1

Reproduce the chart Elliot using the data from the names_year.csv file.

type your results and analysis here

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# Include and execute your code here